Newete, S.W. Phenology-Based Winter Wheat Classification for Crop Growth Monitoring Using Multi-Temporal Sentinel-2 Satellite Data. Preprints2023, 2023060705. https://doi.org/10.20944/preprints202306.0705.v1
APA Style
Newete, S.W. (2023). Phenology-Based Winter Wheat Classification for Crop Growth Monitoring Using Multi-Temporal Sentinel-2 Satellite Data. Preprints. https://doi.org/10.20944/preprints202306.0705.v1
Chicago/Turabian Style
Newete, S.W. 2023 "Phenology-Based Winter Wheat Classification for Crop Growth Monitoring Using Multi-Temporal Sentinel-2 Satellite Data" Preprints. https://doi.org/10.20944/preprints202306.0705.v1
Abstract
The rising global population amidst the growing concerns of climate change will have a dire consequence on global food security and socio-economic activities. Wheat is one of the most important staple foods consumed by more than four billion people in the world, but climate change impacts account for a decline of 5.5% in wheat yield and predictions indicate that the production could further dwindle by nearly 30% in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform government the status of national food security, but also is used to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. A joint project between South Africa and Poland investigated satellite based-crop growth monitoring using Sentinel 2 and determined the most distinguishable crop phenology for an accurate winter wheat classification during the growing season from August – December with Random Forest (RF) algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56%) and producer’s (92.52%) accuracies, despite the relatively lower overall accuracy (78.14%) compared to that of December with OA of 83.58% obtained during the maturity stage. This study, therefore, confirms the suitability of sentinel 2 for an effective phenology-based winter wheat crop classification during the heading stage, reducing the ambiguity of spectral confusion created with surrounding grass and maize crops.
Keywords
Phenology; Tillering; Random Forest; Crop type; Clustering, Unsupervised classification
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
